JP2024037537A - Magnetic resonance imaging apparatus, mr image reconstruction apparatus and mr image reconstruction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain an MR image in which a motion of a subject is highly accurately corrected.

SOLUTION: A magnetic resonance imaging apparatus 1 according to an embodiment includes a sequence control circuit 29 and a processing circuit 51. The sequence control circuit 29 collects time-series k-space data by executing data collection with stack of stars to an imaging portion of a subject. The processing circuit 51 divides the time-series k-space data into a plurality of groups about a time direction, and calculates a motion feature amount expressing a movement degree of the imaging portion on the basis of k-space central part data for each of the plurality of groups. The processing circuit 51 generates post-correction k-space data by correcting k-space data on the basis of the motion feature amount for each of the plurality of groups. The processing circuit 51 reconstructs an MR image about the imaging portion on the basis of the post-correction k-space data about the plurality of groups.

SELECTED DRAWING: Figure 1

COPYRIGHT: (C)2024,JPO&INPIT

Description

Embodiments disclosed in this specification and the drawings relate to a magnetic resonance imaging apparatus, an MR image reconstruction apparatus, and an MR image reconstruction method.

There is a navigator echo method as a technique for correcting the movement of a subject in magnetic resonance imaging. However, by collecting data that is not used for reconstruction, data collection efficiency decreases. Further, as another technique, there is a method of dividing the collected k-space data in the temporal direction and performing three-dimensional registration on the reconstructed image based on each piece of k-space data. However, this method is only applicable to correction of relatively slow motions because it requires a lot of time to collect data in order to obtain sufficient image quality for registration.

Special table 2015-532610 publication

One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to obtain an MR image in which the movement of a subject is corrected with high precision. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems.

The magnetic resonance imaging apparatus according to the embodiment includes an acquisition section, a calculation section, a correction section, and a reconstruction section. The collection unit collects time-series k-space data by performing data collection using a stack of stars on the imaged region of the subject. The calculation unit divides the time-series k-space data into a plurality of groups in the temporal direction, and calculates a motion feature amount representing the degree of movement of the imaged region based on the k-space data at the center of the k-space for each of the plurality of groups. Calculate. The correction unit corrects the k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data. The reconstruction unit reconstructs an MR image regarding the imaging site based on the corrected k-space data regarding the plurality of groups.

FIG. 1 is a diagram showing the configuration of a magnetic resonance imaging apparatus according to this embodiment. FIG. 2 is a diagram showing an overview of Stack of Stars collection. FIG. 3 is a diagram illustrating a processing procedure of image reconstruction processing according to this embodiment. FIG. 4 is a diagram showing data transition in the image reconstruction process shown in FIG. 3. FIG. 5 is a diagram showing an example of dividing time-series k-space data into multiple groups. FIG. 6 is a diagram showing detailed processing procedures for one group in steps S3 to S6. FIG. 7 is a diagram showing a motion feature amount (luminance value centroid) and a central zt image. FIG. 8 is a diagram illustrating an example of correction of the center zt image based on the motion feature amount. FIG. 9 is a diagram showing a comparison between an MR image without motion correction (uncorrected image) and an MR image with motion correction (1×zm and 2×zm). FIG. 10 is a diagram illustrating a motion feature amount and a central zt image according to Modification 1. FIG. 11 is a diagram showing detailed processing procedures of steps S3 to S4 according to modification 2.

Hereinafter, embodiments of a magnetic resonance imaging apparatus, an MR image reconstruction apparatus, and an MR image reconstruction method will be described in detail with reference to the drawings.

FIG. 1 is a diagram showing the configuration of a magnetic resonance imaging apparatus 1 according to this embodiment. As shown in FIG. 1, the magnetic resonance imaging apparatus 1 includes a gantry 11, a bed 13, a gradient magnetic field power supply 21, a transmitting circuit 23, a receiving circuit 25, a bed driving device 27, a sequence control circuit 29, and a host computer 50. has.

The pedestal 11 has a static magnetic field magnet 41 and a gradient magnetic field coil 43. The static magnetic field magnet 41 and the gradient magnetic field coil 43 are housed in the casing of the pedestal 11. A hollow bore is formed in the casing of the pedestal 11 . A transmitting coil 45 and a receiving coil 47 are arranged within the bore of the pedestal 11.

The static magnetic field magnet 41 has a hollow, substantially cylindrical shape, and generates a static magnetic field substantially inside the cylinder. As the static field magnet 41, for example, a permanent magnet, a superconducting magnet, a normal conducting magnet, or the like is used. Here, the central axis of the static magnetic field magnet 41 is defined as the Z-axis, the axis vertically orthogonal to the Z-axis is defined as the Y-axis, and the axis horizontally orthogonal to the Z-axis is defined as the X-axis. The X-axis, Y-axis, and Z-axis constitute an orthogonal three-dimensional coordinate system.

The gradient magnetic field coil 43 is a coil unit that is attached inside the static magnetic field magnet 41 and formed in a hollow, substantially cylindrical shape. The gradient magnetic field coil 43 receives current from the gradient magnetic field power supply 21 and generates a gradient magnetic field. More specifically, the gradient magnetic field coil 43 has three coils corresponding to the X-axis, Y-axis, and Z-axis that are orthogonal to each other. The three coils form a gradient magnetic field whose magnetic field strength changes along each of the X-axis, Y-axis, and Z-axis. The gradient magnetic fields along the X, Y, and Z axes are combined to form a mutually orthogonal slice selection gradient magnetic field Gs, phase encode gradient magnetic field Gp, and frequency encode gradient magnetic field Gr in a desired direction. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section (slice). The phase encoding gradient magnetic field Gp is used to change the phase of a magnetic resonance signal (hereinafter referred to as an MR signal) depending on the spatial position. The frequency encode gradient magnetic field Gr is used to change the frequency of the MR signal depending on the spatial position. In the following description, it is assumed that the gradient direction of the slice selection gradient magnetic field Gs is the Z axis, the gradient direction of the phase encode gradient magnetic field Gp is the Y axis, and the gradient direction of the frequency encode gradient magnetic field Gr is the X axis.

The gradient magnetic field power supply 21 supplies current to the gradient magnetic field coil 43 according to a sequence control signal from the sequence control circuit 29 . The gradient magnetic field power supply 21 supplies current to the gradient magnetic field coils 43, thereby causing the gradient magnetic field coils 43 to generate gradient magnetic fields along the X, Y, and Z axes. The gradient magnetic field is applied to the subject P while being superimposed on the static magnetic field formed by the static magnetic field magnet 41.

The transmitting coil 45 is arranged, for example, inside the gradient magnetic field coil 43, receives current from the transmitting circuit 23, and generates a high frequency pulse (hereinafter referred to as an RF pulse).

The transmitting circuit 23 supplies current to the transmitting coil 45 in order to apply an RF pulse for exciting target protons present in the subject P to the subject P via the transmitting coil 45. The RF pulse oscillates at a resonant frequency specific to the target proton and excites the target proton. An MR signal is generated from the excited target protons and detected by the receiving coil 47. The transmitting coil 45 is, for example, a whole body coil (WB coil). Whole body coils may be used as transmit and receive coils.

The receiving coil 47 receives the MR signal emitted from the target protons present in the subject P under the action of the RF magnetic field pulse. The receiving coil 47 has a plurality of receiving coil elements capable of receiving MR signals. The received MR signal is supplied to the receiving circuit 25 via wire or wirelessly. Although not shown in FIG. 1, the receiving coil 47 has a plurality of receiving channels implemented in parallel. The reception channel includes a reception coil element that receives the MR signal, an amplifier that amplifies the MR signal, and the like. The MR signal is output for each receiving channel. The total number of reception channels and the total number of reception coil elements may be the same, or the total number of reception channels may be greater or less than the total number of reception coil elements.

The receiving circuit 25 receives the MR signal generated from the excited target protons via the receiving coil 47. The receiving circuit 25 processes the received MR signal to generate a digital MR signal. Digital MR signals can be expressed in k-space defined by spatial frequencies. Therefore, hereinafter, the digital MR signal will be referred to as k-space data. K-space data is a type of raw data used for image reconstruction. The k-space data is supplied to the host computer 50 via wire or wireless.

Note that the above-mentioned transmitting coil 45 and receiving coil 47 are merely examples. Instead of the transmitting coil 45 and the receiving coil 47, a transmitting/receiving coil having a transmitting function and a receiving function may be used. Moreover, the transmitting coil 45, the receiving coil 47, and the transmitting/receiving coil may be combined.

A bed 13 is installed adjacent to the pedestal 11. The bed 13 has a top plate 131 and a base 133. A subject P is placed on the top plate 131. The base 133 supports the top plate 131 so as to be slidable along each of the X-axis, Y-axis, and Z-axis. The bed driving device 27 is accommodated in the base 133. The bed driving device 27 moves the top plate 131 under control from the sequence control circuit 29. The bed driving device 27 may include any motor such as a servo motor or a stepping motor.

The sequence control circuit 29 has, as hardware resources, a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), and a memory such as a ROM (Read Only Memory) or a RAM (Random Access Memory). The sequence control circuit 29 synchronously controls the gradient magnetic field power supply 21, the transmission circuit 23, and the reception circuit 25 according to the data collection sequence received from the collection control function 511 of the processing circuit 51, and executes data collection for the subject P. Collect k-space data regarding the subject P. The sequence control circuit 29 is an example of a collection section.

The sequence control circuit 29 according to the present embodiment performs data collection using a stack of stars (hereinafter referred to as stack of stars collection) for the imaged region of the subject to obtain a time series k Collect spatial data. Stack of Stars is a three-dimensional k-space filling scheme that performs two-dimensional radial acquisition on each of multiple slices.

As shown in FIG. 1, the host computer 50 is a computer having a processing circuit 51, a memory 52, a display 53, an input interface 54, and a communication interface 55. The processing circuit 51 is an example of a processing section, the memory 52 is an example of a storage section, the display 53 is an example of a display section, the input interface 54 is an example of an input section, and the communication interface 55 is an example of a communication section. It is.

The processing circuit 51 has a processor such as a CPU as a hardware resource. The processing circuit 51 functions as the core of the magnetic resonance imaging apparatus 1 . For example, the processing circuit 51 has a collection control function 511, an acquisition function 512, a motion feature calculation function 513, a correction function 514, a reconstruction function 515, and a display control function 516 by executing various programs. The collection control function 511 is an example of a collection control unit, the acquisition function 512 is an example of an acquisition unit, the motion feature calculation function 513 is an example of a calculation unit, and the correction function 514 is an example of a correction unit. The configuration function 515 is an example of a reconfiguration unit, and the display control function 516 is an example of a display control unit.

In the collection control function 511, the processing circuit 51 generates a data collection sequence for performing stack of stars collection based on data collection conditions. Data collection conditions are determined manually by a medical professional or the like or automatically by an arbitrary algorithm. The data of the data collection sequence is supplied to the sequence control circuit 29.

In the acquisition function 512, the processing circuit 51 acquires time-series k-space data obtained by stack of stars collection. The processing circuit 51 may obtain the k-space data directly from the receiving circuit 25, or may obtain the k-space data from the memory 52 or the like that stores the k-space data.

In the motion feature calculation function 513, the processing circuit 51 divides the time-series k-space data into a plurality of groups in the temporal direction. The processing circuit 51 calculates, for each of the plurality of groups, a feature amount (hereinafter referred to as a motion feature amount) representing the degree of movement of the imaged region of the subject P, based on the k-space data at the center of the k-space. The motion feature amount is calculated for each group at predetermined time intervals. The predetermined time corresponds to the time resolution of the moving feature amount, and may be appropriately set by an operator such as a medical worker. Typically, it is set based on the time interval of sample points of k-space data. Specifically, for each of the plurality of groups, the processing circuit 51 generates an intermediate image by subjecting the k-space data in the center of the k-space to one-dimensional or two-dimensional Fourier transformation in the k-space direction. A motion feature amount is calculated at predetermined time intervals based on the center of gravity of the brightness values in the image.

In the correction function 514, the processing circuit 51 corrects the k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data. Specifically, the processing circuit 51 executes an intermediate image generated by performing one-dimensional or two-dimensional Fourier transformation in the k-space direction on the k-space data in the central part of the k-space for each of the plurality of groups. Corrected k-space data is generated by deforming the image in the spatial direction according to the motion feature amount and subjecting the deformed intermediate image to one-dimensional or two-dimensional inverse Fourier transformation in the k-space direction.

In the reconstruction function 515, the processing circuit 51 reconstructs an MR image regarding the imaged part of the subject P based on the corrected k-space data generated by the correction function 514. As the reconstruction method, any method for reconstructing an MR image from k-space data obtained by non-Cartesian acquisition may be used. As such a reconstruction method, for example, gridding reconstruction, NUFFT (non-uniform fast fourier transform), machine learning reconstruction, etc. can be used. Furthermore, the processing circuit 51 performs various image processing on the reconstructed image. For example, the processing circuit 51 may perform image processing such as volume rendering, surface rendering, pixel value projection processing, MPR (Multi-Planer Reconstruction) processing, and CPR (Curved MPR) processing.

In the display control function 516, the processing circuit 51 displays various information on the display 53. For example, the processing circuit 51 displays the MR image etc. generated by the reconstruction function 515 on the display 53. The processing circuit 51 may appropriately perform display processing such as gradation processing, enlargement/reduction processing, and annotation on the MR image.

The memory 52 is a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or an integrated circuit storage device that stores various information. Further, the memory 52 may be a drive device or the like that reads and writes various information to/from a portable storage medium such as a CD-ROM drive, a DVD drive, or a flash memory. For example, the memory 52 stores imaging conditions, k-space data, MR images, image reconstruction programs, and the like.

The display 53 displays various information using a display control function 516. For example, the display 53 displays an MR image etc. generated by the reconstruction function 515. As the display 53, for example, a CRT display, a liquid crystal display, an organic EL display, an LED display, a plasma display, or any other display known in the art can be used as appropriate.

The input interface 54 includes input devices that accept various commands from the user. As input devices, keyboards, mice, various switches, touch screens, touch pads, etc. can be used. Note that the input device is not limited to those equipped with physical operation parts such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device provided separately from the magnetic resonance imaging apparatus 1 and outputs the received electrical signals to various circuits is also input. Included in the example of interface 54. The input interface 54 may also be a voice recognition device that converts voice signals collected by a microphone into instruction signals.

The communication interface 55 connects the magnetic resonance imaging apparatus 1 to a workstation, a PACS (Picture Archiving and Communication System), an HIS (Hospital Information System), an RIS (Radiology Information System), etc. via a LAN (Local Area Network) or the like. This is the interface to connect. The network IF sends and receives various information to and from connected workstations, PACS, HIS, and RIS.

Note that the above configuration is an example and is not limited thereto. For example, the sequence control circuit 29 may be incorporated into the host computer 50, or the sequence control circuit 29 and the processing circuit 51 may be mounted on the same board. As another example, there is no need for a one-to-one correspondence between the mount 10 and the host computer 50; one host computer may be provided for a plurality of mounts, or a plurality of host computers may be provided for one mount. A host computer may also be provided.

The magnetic resonance imaging apparatus 1 according to the present embodiment performs motion correction of the imaged region of the subject based on k-space data obtained by stack of stars acquisition. Here, an overview of Stack of Stars collection will be explained with reference to FIG. 2.

FIG. 2 is a diagram showing an overview of Stack of Stars collection. As shown in the upper part of FIG. 2, the k-space is represented as a three-dimensional space defined by spatial frequencies kx, ky, and kz. A substantially cylindrical data collection range is set in k-space. Stack of Stars collection is performed along a k-space trajectory called spoke Kn1 (n is a subscript of the angle of the spoke. It is the same as the subscript representing a group, which will be described later). The spoke Kn1 is horizontal to the kxky plane and passes through the center of the kxky plane within the data collection range of k space. The k-space axis parallel to the spoke Kn1 is also called the RO axis, and the k-space axis orthogonal to the spoke Kn1 is also called the SL axis. In the stack of stars acquisition, k-space data is collected at predetermined sampling intervals along the spoke Kn1 or RO axis. The angle formed between the center point of the kzky plane and the spoke Kn1 forms a spoke angle.

As an example, data collection is performed for the spoke K11 at an arbitrary spoke angle in the kxky plane while sequentially changing the kz position (slice position). When data collection is performed at all kz positions for the spoke K11 of the same spoke angle, data collection is similarly performed at all kz positions for the spoke K21 of the next spoke angle. In this way, data collection is performed for all spoke angles while sequentially changing the kz position. The number n of spoke angles is not limited to four, but may be any number greater than or equal to one. The current spoke angles may be set, for example, according to the golden angle rule so that they are uniformly distributed in the time direction in k-space. Here, a set of all spokes Kn1 having the same angle will be called a group Kn.

As shown in the lower part of FIG. 2, MR images regarding a plurality of kz positions (slice positions) are reconstructed based on the k-space data of all groups K1, K2, K3, and K4. When all spokes Kn1 are aggregated into one group K0, these spokes Kn1 look like a stacked star. For this reason, this data collection method is called Stack of Stars. As mentioned above, the stack of stars collection has the property of being robust to motion artifacts since each spoke passes through or near the center of the kxky plane.

Next, a processing procedure for image reconstruction according to this embodiment will be explained.

FIG. 3 is a diagram illustrating a processing procedure of image reconstruction processing according to this embodiment. FIG. 4 is a diagram showing data transition in the image reconstruction process shown in FIG. 3.

As shown in FIGS. 3 and 4, first, the processing circuit 51 implements the collection control function 511 to perform data collection using a stack of stars to collect time-series k-space data (step S1). . When step S1 is performed, the processing circuit 51 divides the time-series k-space data collected in step S1 into a plurality of groups in the temporal direction by implementing the motion feature calculation function 513 (step S2).

FIG. 5 is a diagram showing an example of dividing time-series k-space data into multiple groups Kn. As shown in FIG. 5, the processing circuit 51 divides the time-series k-space data obtained by stack of stars collection into a plurality of groups Kn. The number of groups n is not particularly limited as long as it is a natural number of 2 or more. FIG. 5 illustrates an example where the number of groups is n=4.

Group Kn is formed by sequentially dividing time-series k-space data along the time direction. As an example, each group Kn is set to include a plurality of spokes Kn1 having the same angle. As will be described later, since it is necessary to perform FFT in the one-dimensional or two-dimensional k-space direction for each group Kn, the number of spokes Kn1 included in each group Kn needs to be two or more.

Each group Kn has k-space data of the k-space center Kn2 across the plurality of spokes Kn1. The k-space center Kn2 means the center in the kxky plane. That is, the k-space central portion Kn2 does not have to be the central portion with respect to the kz axis. The k-space central portion Kn2 may include not only the center (kx=ky=0) with respect to the kxky plane but also a local region including the center. In other words, the k-space central portion Kn2 may be a one-dimensional region in the three-dimensional k-space, or may be a two-dimensional region. Hereinafter, it is assumed that the central part Kn2 of the k-space is a one-dimensional region. Further, the k-space data of the k-space central portion Kn2 will be referred to as k-space central portion data.

After step S2 is performed, the processing circuit 51 realizes the motion feature amount calculation function 513 to calculate a motion feature amount based on the k-space center data for each of the plurality of groups (step S3). Specifically, in step S3, the processing circuit 51 performs one-dimensional Fourier transform on the k-space center data in the k-space direction for each of the plurality of groups to generate an intermediate image, and calculates the brightness in the intermediate image. A motion feature amount is calculated at predetermined time intervals based on the center of gravity of the value. When step S3 is performed, the processing circuit 51 implements the correction function 514 to perform correction on the corrected k-space based on the k-space data collected in step S1 and the motion feature amount calculated in step S3 for each of the plurality of groups. Generate data (step S4). Specifically, in step S4, the processing circuit 51 deforms the intermediate image for each of the plurality of groups according to the motion feature amount in the real space direction, and converts the deformed intermediate image into a one-dimensional one-dimensional image in the k-space direction. Corrected k-space data is generated by inverse Fourier transformation.

FIG. 6 is a diagram showing the detailed processing procedure of steps S3 to S4. FIG. 6 illustrates a processing procedure for one group. As shown in FIG. 6, in step S3, the processing circuit 51 first identifies the k-space center data of the k-space data, and performs a Fourier transform (zFFT) in the kz direction on the k-space center data. A zt image, which is an intermediate image, is reconstructed (step S31). A zt image is an image in which a brightness value corresponding to a k-space data value (MR signal intensity) is assigned to each point on a two-dimensional plane defined by a real space z-axis and a time axis t. Since the zt image reconstructed in step S31 is based on the k-space center data, it will be referred to as the center zt image. The central zt image is based on low frequency components in the kx and ky directions of the k-space data, and reflects the global movement characteristics of the MR signal in the z-direction.

When step S31 is performed, the processing circuit 51 calculates a motion feature amount by calculating the center of gravity of the luminance value on the central zt image (step S32). The motion feature amount is a feature amount that reflects the degree of movement of each anatomical region included in the imaged region. In step S32, the processing circuit 51 performs a centroid calculation of the luminance value on the center zt image at predetermined time intervals, and calculates the luminance value centroid for each predetermined time as a motion feature amount. The predetermined period of time means a period of time during which the motion feature amount is calculated. Hereinafter, the predetermined time will be referred to as calculation target time. The calculation target time may be set to the time interval between adjacent sample points of the k-space data, or may be set to an arbitrary time larger than the time interval. The luminance value centroid over a plurality of times represents a change in the luminance value centroid over time. The temporal change in the luminance value centroid is described by the luminance value centroid zm(t) with time as a variable. The luminance value centroid zm(t) is calculated based on the luminance value of the center zt image according to (1) below. The denominator of equation (1) is the integral value of the brightness value I (z, t) with respect to the real space z value, and the numerator is the integral value of the brightness value I (z, t) with respect to the real space z value. It is an integral value. The brightness value centroid zm(t) represents the temporal fluctuation in the real space z direction of a major part in terms of brightness value within the imaged part.

FIG. 7 is a diagram showing the central zt image I1 and the motion feature amount (luminance value center of gravity) I11. As shown in FIG. 7, in the center zt image I1, the vertical axis is defined by the real space z position, the horizontal axis is defined by the acquisition time, and each pixel has a data value (MR signal intensity) of the k space center data. This is an image to which a brightness value is assigned according to the . The motion feature amount I11 represents a temporal change in the luminance value center of gravity.

Here, it is assumed that the imaging site according to this embodiment is the abdomen including the liver. In this case, the high brightness area in the central zt image I1 represents the liver area. The central zt image I1 visualizes the positional fluctuation of the liver region in the real space z direction. The motion feature amount I11 represents a temporal change in the luminance value center of gravity in the real space z-direction of the liver motion region.

As shown in FIG. 6, the processing circuit 51 performs zFFT on the k-space data to generate a zt image in parallel to steps S31 and S32 (step S41). The zt image generated in step S41 is based on all frequency components of the k-space data. When step S41 is performed, the processing circuit 51 generates a corrected zt image based on the zt image generated in step S41 and the motion feature amount calculated in step S32.

FIG. 8 is a diagram illustrating an example of correction of a zt image based on motion feature amounts. The upper part of FIG. 8 illustrates the uncorrected zt image I2 on which the motion feature amount I21 is superimposed. The processing circuit 51 corrects the zt image I2 by geometrically deforming the zt image I2 in units of calculation target time according to a numerical value corresponding to the motion feature amount. The processing circuit 51 transforms the zt image I2 so that the luminance value gravity center I31 based on the corrected zt image I3 becomes flat. The mode of deformation may be appropriately selected from parallel movement, enlargement, or reduction.

Here, it is assumed that the mode of deformation is parallel movement (shift) in the z-direction of real space. In this case, the processing circuit 51 corrects the zt image I2 by shifting the zt image I2 in the z direction in accordance with a numerical value (hereinafter referred to as shift distance) corresponding to the motion feature amount zm(t) in units of calculation target time. The shift distance is set to a value obtained by multiplying the motion feature amount zm(t) by the weight value α. The weight value α may be arbitrarily set depending on the region to be imaged, the case to be diagnosed, and the like. It is assumed that the weight value α is set to a numerical value of approximately 0.5 to 2.0, but is not limited to this and may be set to any numerical value. Further, the shift distance may be any numerical value based on the motion feature amount zm(t), such as a moving average of the motion feature amount zm(t).

By shifting the zt image I2, if a pixel with a missing luminance value (hereinafter referred to as a missing pixel) occurs at the end of the zt image I2 in the z direction on the opposite side to the shift direction, the missing pixel will have a zero value, etc. It is recommended to assign an arbitrary brightness value. Alternatively, the luminance values of pixels that deviate from the zt image I2 depending on the shift direction may be cyclically assigned to the missing pixels. The method of filling the luminance value of the missing pixel may be arbitrarily selected from the methods described above.

After step S42 is performed, the processing circuit 51 generates corrected k-space data by subjecting the corrected zt image to an inverse FFT in the real space z direction (hereinafter referred to as inverse zFFT) (step S43). As described above, the process shown in FIG. 6 is executed for each of the plurality of groups, and corrected k-space data is generated for each of the plurality of groups.

As shown in FIGS. 3 and 4, when step S4 is performed, the processing circuit 51 realizes the reconstruction function 515 to reconstruct an MR image based on the corrected k-space data regarding a plurality of groups (step S5). . As the reconstruction method in step S5, any method for reconstructing an image in the Cartesian coordinate system from k-space data in the stack of stars coordinate system may be used. The stack of stars coordinate system is a coordinate system defined by the kz axis, the RO axis, and the angle formed by the kz axis and the RO axis. For example, gridding reconstruction is known as a reconstruction method that can be used in step S5. In the gridding reconstruction, the processing circuit 51 arranges a plurality of corrected k-space data regarding a plurality of groups expressed in a stack of stars coordinate system in a three-dimensional space of a Cartesian coordinate system, and Based on the data value of each sampling point of the corrected k-space data, the data value of each grid point in the three-dimensional space of the Cartesian coordinate system is calculated. This results in k-space data aligned to the Cartesian coordinate system. The processing circuit 51 then performs FFT on the k-space data aligned in the Cartesian coordinate system to reconstruct a three-dimensional MR image.

When step S5 is performed, the processing circuit 51 displays the MR image reconstructed in step S5 by realizing the display control function 516 (step S6). In step S6, the processing circuit 51 displays the reconstructed MR image on the display 53. In addition to the MR image, the processing circuit 51 may display the center zt image generated in step S31, the zt image generated in step S41, and the corrected zt image generated in step S42. The processing circuit 51 may display a curve corresponding to the motion feature amount superimposed on the center zt image, zt image, or corrected zt image.

With the above steps, the image reconstruction process according to this embodiment is completed.

FIG. 9 is a diagram showing a comparison between an MR image without motion correction (uncorrected image) and an MR image with motion correction. Examples of MR images with motion correction include an MR image (1×zm) corrected with a weight value α=1 and an MR image (2×zm) corrected with a weight value α=2. FIG. 9 illustrates the above three types of MR images for each of Case 1 and Case 2. Case 1 and Case 2 both use the abdomen including the liver as the imaging site, and are different patients. In case 1, a strong motion artifact appears in the liver in the uncorrected image, but the motion artifact is reduced in the 1×zm and 2×zm MR images. In case 2, strong motion artifacts appear in the liver in the uncorrected image, but the motion artifacts are reduced in the 1×zm and 2×zm MR images. Therefore, according to the image reconstruction processing described above, it is possible to reconstruct an MR image of good image quality with reduced motion artifacts.

Note that the image reconstruction processing described above is an example, and any additions, deletions, and/or changes can be made without departing from the scope of the invention.

(Modification 1)
In the image reconstruction processing described above, the motion feature amount is the luminance value centroid of the central zt image. However, this embodiment is not limited to this. The processing circuit 51 according to the first modification performs one-dimensional or two-dimensional Fourier transform on the k-space central part data in the k-space direction for each of the plurality of groups to generate a central part zt image, and A motion feature amount is calculated at predetermined time intervals based on edges of specific brightness value regions in the image.

FIG. 10 is a diagram illustrating a motion feature amount and a central zt image according to modification 1. As shown in FIG. 10, the processing circuit 51 performs image processing on the central zt image to detect edges in a specific luminance value region of the central zt image. As an example, the processing circuit 51 performs threshold processing using a predetermined threshold value on the center zt image and extracts an image region I40 having a luminance value higher than the threshold value (hereinafter referred to as a high luminance value region). The high luminance value area I40 is a white image area shown in FIG. Next, the processing circuit 51 extracts an upper end (hereinafter referred to as upper edge) I41 or a lower end (hereinafter referred to as lower edge) I42 in the z direction of the high luminance value region I40. Then, the processing circuit 51 sets the upper edge I41 or the lower edge I42 at each acquisition time as a motion feature amount. According to the first modification, it is possible to provide variations in the motion feature amount, and it is possible to select a method for calculating the motion feature amount depending on the imaging region, required image quality, processing load, and the like.

Note that the motion feature amount according to Modification 1 is not limited to the above example. As an example, the processing circuit 51 may calculate statistical values such as an average value or an intermediate value between the upper edge I41 and the lower edge I42 as the motion feature quantity. Furthermore, the method of calculating the upper edge I41 and the lower edge I42 is not limited to the above method. For example, the processing circuit 51 may extract the upper edge I41 and/or the lower edge I42 by performing edge detection of the brightness value on the central zt image I4. Alternatively, the processing circuit 51 may perform edge detection using machine learning on the central zt image I4 to extract the upper edge I41 and/or the lower edge I42.

The specific brightness value area according to modification example 1 is not limited to a high brightness value area. As an example, the specific brightness value region may be an image region of the central zt image that is less than the above threshold value (low brightness value region), or may be an image region corresponding to a designated anatomical region.

(Modification 2)
In the image reconstruction process described above, the processing circuit 51 generates corrected k-space data by correcting the zt image based on the motion feature amount. However, this embodiment is not limited to this. The processing circuit 51 according to the second modification generates corrected k-space data by adding a phase gradient to the k-space center data based on the motion feature amount.

FIG. 11 is a diagram showing detailed processing procedures of steps S3 to S4 according to modification 2. FIG. 11 illustrates a processing procedure for one group. Note that the same reference numerals are given to the same processes as in FIG. 6 to simplify the explanation. As shown in FIG. 11, in step S3, the processing circuit 51 first identifies the k-space center data from the k-space data, performs zFFT on the k-space center data, and reproduces the center zt image. Configure (step S31). Next, the processing circuit 51 calculates a motion feature amount by calculating the center of gravity of the brightness value on the central zt image (step S32). Note that the motion feature amount may be a feature amount based on an edge of a specific luminance value area according to the first modification.

The processing circuit 51 then performs motion correction on the k-space data of each group based on the motion feature amount (step S44). As a result, corrected k-space data is generated. Specifically, the processing circuit 51 applies a phase gradient of a numerical value obtained by multiplying the motion feature amount by the weight value α to the data value of each sample point constituting the k-space data of each group. The phase gradient is given by adding a phase value corresponding to a value multiplied by the weight value α to the phase value of the data value of each sample. Adding a phase gradient to the k-space data in step S44 is mathematically equivalent to shifting the image in the spatial direction to the zt image. After that, the processing circuit 51 reconstructs the MR image based on the plurality of corrected k-space data regarding the plurality of groups (step S5). According to modification example 2, it is possible to have variations in motion correction of k-space data based on motion features, and it is possible to select a motion correction method according to the imaging region, required image quality, processing load, etc. Become.

(Modification 3)
In the image reconstruction processing described above, the central part of the k-space is assumed to be a one-dimensional region. However, this embodiment is not limited to this. The k-space center need only be of lower dimension than the collected k-space data. Since the k-space data related to Stack of Stars is three-dimensional, the central part of the k-space according to the third modification may be a one-dimensional region or a two-dimensional region in the three-dimensional k-space. As an example, the two axes constituting the two-dimensional region may be selected as the kz axis and the RO axis. In this case, the zt image can be reconstructed by performing FFT regarding the two axes on the k-space data.

(Modification 4)
In the image reconstruction processing described above, the processing circuit 51 uses the correction function 514 to translate (shift) the zt image in the real space z direction based on the motion feature amount. However, this embodiment is not limited to this. The processing circuit 51 according to the fourth modification may enlarge or reduce the zt image in the z direction based on the motion feature amount. Specifically, the processing circuit 51 enlarges or reduces the zt image at a magnification based on α×zm(t) for each calculation target time. The portion that protrudes from the zt image due to enlargement may be deleted. A predetermined luminance value may be assigned to pixels missing due to reduction. The processing circuit 51 may combine enlargement or reduction and parallel movement.

(Modification 5)
The image reconstruction process described above was performed by the magnetic resonance imaging apparatus 1 having the gantry 11 and the sequence control circuit 29. However, this embodiment is not limited to this. The image reconstruction process described above can also be performed by an image reconstruction device that can acquire time-series k-space data collected by performing stack of stars collection on the photographed region of the subject. The image reconstruction device according to Modification 5 may be implemented, for example, as a computer in which the collection control function 511 is removed from the host computer 51.

(Summary)
According to the several embodiments described above, the magnetic resonance imaging apparatus 1 according to the present embodiment includes the sequence control circuit 29 and the processing circuit 51. The sequence control circuit 29 collects time-series k-space data by collecting data using a stack of stars for the imaged region of the subject. The processing circuit 51 divides the time-series k-space data into a plurality of groups in the temporal direction, and for each of the plurality of groups, calculates a motion feature representing the degree of movement of the imaged region based on the k-space center data at predetermined time intervals. calculate. The processing circuit 51 corrects the k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data. The processing circuit 51 reconstructs an MR image regarding the imaging site based on the corrected k-space data regarding the plurality of groups.

According to the above configuration, the motion feature amount is calculated using the k-space center data smaller than the k-space data collected by Stack of Stars, and the corrected k-space data is generated based on the motion feature amount. . This makes it possible to perform correction that can follow even relatively fast movements, compared to when motion correction is performed by performing three-dimensional registration on the reconstructed image. Further, by performing reconstruction using the corrected k-space data regarding all groups, it becomes possible to obtain a high-quality MR image. Furthermore, according to the above configuration, self-navigation that obtains motion features from the collected k-space data becomes possible, so data collection efficiency can be improved compared to the case where separate sensor data, etc. are used. becomes possible.

According to at least one embodiment described above, it is possible to obtain an MR image in which the movement of the subject is corrected with high precision.

The term "processor" used in the above description refers to, for example, a CPU, GPU, or Application Specific Integrated Circuit (ASIC), or a programmable logic device (for example, a Simple Programmable Logic Device). :SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA). A processor realizes its functions by reading and executing a program stored in a memory circuit. Note that instead of storing the program in the memory circuit, the program may be directly incorporated into the circuit of the processor. In this case, the processor realizes its functions by reading and executing a program built into the circuit. On the other hand, when the processor is, for example, an ASIC, instead of storing the program in a storage circuit, the function is directly incorporated into the processor's circuitry as a logic circuit. Note that each processor of this embodiment is not limited to being configured as a single circuit for each processor, but may also be configured as a single processor by combining multiple independent circuits to realize its functions. good. Furthermore, a plurality of components in FIG. 1 may be integrated into one processor to realize its functions.

Although several embodiments have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, changes, and combinations of embodiments can be made without departing from the gist of the invention. These embodiments and their modifications are included within the scope and gist of the invention as well as within the scope of the invention described in the claims and its equivalents.

1 Magnetic resonance imaging apparatus 11 Mount 13 Bed 21 Gradient magnetic field power supply 23 Transmission circuit 25 Receiving circuit 27 Bed driving device 29 Sequence control circuit 41 Static magnetic field magnet 43 Gradient magnetic field coil 45 Transmitting coil 47 Receiving coil 50 Host computer 51 Processing circuit 52 Memory 53 Display 54 Input interface 55 Communication interface 131 Top plate 133 Base 511 Collection control function 512 Acquisition function 513 Motion feature calculation function 514 Correction function 515 Reconstruction function 516 Display control function

Claims (11)

a collection unit that collects time-series k-space data by collecting data using a stack of stars for the imaged region of the subject;
Divide the time-series k-space data into a plurality of groups in the temporal direction, and calculate a motion feature representing the degree of movement of the imaging region based on the k-space data in the central part of the k-space for each of the plurality of groups. Department and
a correction unit that corrects k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data;
a reconstruction unit that reconstructs an MR image regarding the imaging site based on the corrected k-space data regarding the plurality of groups;
A magnetic resonance imaging device comprising: The calculation unit generates an intermediate image by performing one-dimensional or two-dimensional Fourier transformation in the k-space direction on the k-space data in the central part of the k-space for each of the plurality of groups, and calculates the brightness in the intermediate image. The magnetic resonance imaging apparatus according to claim 1, wherein the motion feature amount is calculated at predetermined time intervals based on the center of gravity of the value. The calculation unit generates an intermediate image by subjecting the k-space data in the center of the k-space to one-dimensional or two-dimensional Fourier transformation in the k-space direction for each of the plurality of groups, and 2. The magnetic resonance imaging apparatus according to claim 1, wherein the motion feature amount is calculated at predetermined time intervals based on edges of a brightness value region. The correction unit converts, for each of the plurality of groups, an intermediate image generated by performing one-dimensional or two-dimensional Fourier transformation in the k-space direction on the k-space data in the central part of the k-space in the real space direction. 2. The magnetic field according to claim 1, wherein the corrected k-space data is generated by deforming the intermediate image according to the motion feature amount and subjecting the deformed intermediate image to one-dimensional or two-dimensional inverse Fourier transform in the k-space direction. Resonance imaging device. 5. The magnetic resonance imaging apparatus according to claim 4, wherein the deformation includes at least one of parallel movement and expansion or contraction. The magnetic resonance imaging apparatus according to claim 1, wherein the correction unit generates the corrected k-space data by adding a phase gradient to the k-space data in the central part of the k-space based on the motion feature amount. . 5. The magnetic resonance imaging apparatus according to claim 4, wherein the correction unit performs the deformation according to a value obtained by multiplying the motion feature amount by a predetermined weight value. The magnetic resonance imaging apparatus according to claim 1, wherein the central part of k-space is a one-dimensional region in three-dimensional k-space. The magnetic resonance imaging apparatus according to claim 1, wherein the central part of k-space is a two-dimensional region in three-dimensional k-space. an acquisition unit that acquires time-series k-space data by collecting data using a stack of stars for the imaged region of the subject;
Divide the time-series k-space data into a plurality of groups in the temporal direction, and calculate a motion feature representing the degree of movement of the imaging region based on the k-space data in the central part of the k-space for each of the plurality of groups. Department and
a correction unit that corrects k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data;
a reconstruction unit that reconstructs an MR image regarding the imaging site based on the corrected k-space data regarding the plurality of groups;
An MR image reconstruction device comprising: Execute data collection using Stack of Stars for the imaged part of the subject to obtain time-series k-space data,
dividing the time-series k-space data into a plurality of groups in the time direction;
For each of the plurality of groups, a motion feature amount representing the degree of movement of the imaging region is calculated at predetermined time intervals based on k-space data of a central part of k-space;
correcting k-space data for each of the plurality of groups based on the motion feature amount to generate corrected k-space data;
reconstructing an MR image regarding the imaging site based on the corrected k-space data regarding the plurality of groups;
An MR image reconstruction method comprising: